On Sun, 2022-01-23 at 14:12 -0800, Brock Mendel wrote: > Have any of the numpy devs weighed in on this? If an efficient > version of > this were available in numpy there is a lot of pandas code I would > enjoy > ripping out.
As this is a Python float method, that is an argument for implementing
it without much thought – at least so long it mostly aligns with
Python.
If we are worried about arbitrary precision integers, maybe we should
go with "index" and limit ourselves to "indexable integers"?
(For such a method it may make sense if 2**60 would be rejected as it
is within the range where double spacing is larger than 1, but not sure
what speed implications that would have.)
Generally, `dtype=...` argument seems a bit much of a zoo of
possibilities to me. Because `int64.is_integer(np.uint8)` would make a
lot of sense suddenly.
To answer the question about ufuncs: No you can't use ufuncs without
first inventing and implementing new API that can deal with this
reasonably (unless you make a family of ufuncs for each `dtype=...`).
That part is easier now than it once was, but that doesn't make it
easy...
A try/except approach is maybe a bit more "accessible" in this regard.
Since you could make a ufunc:
np.try_cast_safely(arr, dtype=np.uint8)
That assumes things will work out but raises an error if they don't.
A probably weird possibility to do "int" specific, would be:
# cast to intp array, but do it "differently":
arr.astype(Indexable, copy=False)
Where Indexable is a special DType. That requires only a very minor
API extension.
I don't mind that extension, but it is probably a bit too strange that
the `Indexable` indicates a "fail on loss of precision" mode.
Cheers,
Sebastian
>
> On Sun, Jan 2, 2022 at 11:16 AM Joseph Fox-Rabinovitz <
> jfoxrabinov...@gmail.com> wrote:
>
> > Is there a guide on how to pacakage non-ufunc functions with
> > multiple
> > loops? Something like sort? It looks like there is no way of adding
> > additional arguments to a ufunc as of yet.
> >
> > On a related note, would it be more useful to have a function that
> > returns
> > the number of bits required to store a number, or -1 if it has a
> > fractional
> > part? Then you could just test something like ``(k :=
> > integer_bits(a)) < 64
> > & k > 0``.
> >
> > - Joe
> >
> >
> > On Sat, Jan 1, 2022 at 5:55 AM Joseph Fox-Rabinovitz <
> > jfoxrabinov...@gmail.com> wrote:
> >
> > > Stefano,
> > >
> > > That is an excellent point. Just to make sure I understand, would
> > > an
> > > interface like `is_integer(a, int_dtype=None)` be satisfactory?
> > > That way,
> > > there are no bounds by default (call it python integer bounds),
> > > but the
> > > user can specify a limited type at will. An alternative would be
> > > something
> > > like `is_integer(a, bits=None, unsigned=False)`. This would have
> > > the
> > > advantage of testing against hypothetical types, which might be
> > > useful
> > > sometimes, or just annoying. I could always allow a two-element
> > > tuple in as
> > > an argument to the first version.
> > >
> > > While I completely agree with the idea behind adding this test,
> > > one big
> > > question remains: can I add arbirary arguments to a ufunc?
> > >
> > > - Joe
> > >
> > > On Sat, Jan 1, 2022 at 5:41 AM Stefano Miccoli
> > > <stefano.micc...@polimi.it>
> > > wrote:
> > >
> > > > I would rather suggest .is_integer(integer_dtype) signature
> > > > because
> > > > knowing that 1e300 is an integer is not very useful in the
> > > > numpy world,
> > > > since this integer number is not representable as a
> > > > numpy.integer dtype.
> > > >
> > > > Note that in python
> > > >
> > > > assert not f.is_integer() or int(f) == f
> > > >
> > > > never fails because integers have unlimited precision but this
> > > > does
> > > > would not map into
> > > >
> > > > assert ( ~f_arr.is_integer() | (np.int64(f_arr) == f.arr)
> > > > ).all()
> > > >
> > > > because of possible OverflowErrors.
> > > >
> > > > Stefano
> > > >
> > > > On 31 Dec 2021, at 04:46,
> > > > numpy-discussion-requ...@python.org wrote:
> > > >
> > > > Is adding arbitrary optional parameters a thing with ufuncs? I
> > > > could
> > > > easily add upper and lower bounds checks.
> > > >
> > > > On Thu, Dec 30, 2021, 20:56 Brock Mendel
> > > > <jbrockmen...@gmail.com> wrote:
> > > >
> > > > > At least some of the commenters on that StackOverflow page
> > > > > need a
> > > > > slightly stronger check: not only is_integer(x), but also
> > > > > "np.iinfo(dtype).min <= x <= np.info(dtype).max" for some
> > > > > particular
> > > > > dtype. i.e. "Can I losslessly set these values into the
> > > > > array I already
> > > > > have?"
> > > > >
> > > > >
> > > > >
> > > >
> > > > _______________________________________________
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> > > >
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> >
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